article

Precisiated natural language (PNL)

Author:

Lotfi A. ZadehAuthors Info & Claims

AI Magazine, Volume 25, Issue 3

Pages 74 - 91

Published: 01 September 2004 Publication History

Abstract

This article is a sequel to an article title "A New Direction in AI--Toward a Computational Theory of Perceptions," which appeared in the Spring 2001 issue of AI Magazine (volume 22, No. 1, 73-84). The concept of precisiated natural language (PNL) was briefly introduced in that article, and PNL was employed as a basis for computation with perceptions. In what follows, the conceptual structure of PNL is described in greater detail, and PNL's role in knowledge representation, deduction, and concept definition is outlined and illustrated by examples. What should be understood is that PNL is in its initial stages of development and that the exposition that follows is an outline of the basic ideas that underlie PNL rather than a definitive theory.

References

[1]

Allan, K. 2001. Natural Language Semantics. Oxford: Oxford Blackwell Publishers.

[2]

Barwise, J.; and Cooper, R., 1981. Generalized Quantifiers and Natural Language. Linguistics and Philosophy 4(1): 159-209.

[3]

Biermann, A. W.; and Ballard, B. W., 1980. Toward Natural Language Computation. American Journal of Computational Linguistics (6)2: 71-86.

[4]

Corcho, O., Fernandez-Lopez, M.; and Gomez-Perez, A. 2003. Methodologies, Tools and Languages for Building Ontologies. Where Is Their Meeting Point? Data and Knowledge Engineering 46(1): 41-64.

Digital Library

[5]

Cresswell, M. J. 1973. Logic and Languages. London: Methuen.

[6]

Davis, E. 1990. Representations of Commonsense Knowledge. San Francisco: Morgan Kaufmann.

[7]

Dubois, D., and Prade, H. 1996. Approximate and Commonsense Reasoning: From Theory to Practice. In Proceedings of the Foundations of Intelligent Systems. Ninth International Symposium, 19-33. Berlin: Springer-Verlag.

[8]

Fellbaum, C., ed. 1998. WordNet: An Electronic Lexical Database. Cambridge, Mass.: The MIT Press.

[9]

Fuchs, N. E.; and Schwertel, U. 2003. Reasoning in Attempto Controlled English. In Proceedings of the Workshop on Principles and Practice of Semantic Web Reasoning (PPSWR 2003), 174-188. Lecture Notes in Computer Science. Berlin: Springer-Verlag.

[10]

Gamat, T. F. 1996. Language, Logic and Linguistics. Chicago: University of Chicago Press.

[11]

Gerla, G. 2000. Fuzzy Metalogic for Crisp Logics. In Discovering the World with Fuzzy Logic: Studies in Fuzziness and Soft Computing (57), ed. V. Novak and I. Perfilieva, 175-187. Heidelberg: Physica-Verlag.

[12]

Haack, S. 1996. Deviant Logic, Fuzzy Logic: Beyond the Formalism. Chicago: University of Chicago Press.

[13]

Haack, S. 1974. Deviant Logic. Cambridge: Cambridge University Press.

[14]

Hajek, P. 2000. Many. In Discovering the World with Fuzzy Logic: Studies in Fuzziness and Soft Computing (57), ed. V. Novak and I. Perfilieva, 302-304. Heidelberg: Physica-Verlag.

[15]

Hajek, P. 1998. Metamathematics of Fuzzy Logic: Trends in Logic (4). Dordrecht, The Netherlands: Kluwer Academic Publishers.

[16]

Kaufmann A.; and Gupta, M. M. 1985. Introduction to Fuzzy Arithmetic: Theory and Applications. New York: Van Nostrand.

[17]

Klein, E. 1980. A Semantics for Positive and Comparative Adjectives. Linguistics and Philosophy 4(1): 1-45.

[18]

Klement, P.; Mesiar, R.; and Pap, E. 2000. Triangular Norms--Basic Properties and Representation Theorems. In Discovering the World with Fuzzy Logic: Studies in Fuzziness and Soft Computing (57), ed. V. Novak and I. Perfilieva, 63-80. Heidelberg: Physica-Verlag.

[19]

Klir, G. J. 2000. Uncertainty-Based Information: A Critical Review. In Discovering the World with Fuzzy Logic: Studies in Fuzziness and Soft Computing (57), ed. V. Novak and I. Perfilieva, 29-50. Heidelberg: Physica-Verlag.

[20]

Lehmke, S. 2000. Degrees of Truth and Degrees of Validity. In Discovering the World with Fuzzy Logic: Studies in Fuzziness and Soft Computing (57), ed. V. Novak and I. Perfilieva, 192-232. Heidelberg: Physica-Verlag.

[21]

Lehnert, W. G. 1978. The Process of Question Answering--A Computer Simulation of Cognition . Hillsdale, New Jersey: Lawrence Eftbaum Associates.

[22]

Lenat, D. B. 1995. CYC: A Large-Scale Investment in Knowledge Infrastructure. Communications of the ACM 38(11): 32-38.

Digital Library

[23]

Liu, H.; and Singh, P. 2004. Commonsense Reasoning in and Over Natural Language. In Proceedings of the Eighth International Conference on Knowledge-Based Intellligent Information and Engineering Systems. Brighton, U.K.: KES Secretariat, Knowledge Transfer Partnership Centre.

[24]

Macias, B., and Stephen G. Pulman. 1995. A Method for Controlling the Production of Specifications in Natural Language. The Computing Journal 38 (4): 310-318.

[25]

Mani, I., and M. T. Maybury, eds. 1999. Advances in Automatic Text Summarization. Cambridge, Mass.: The MIT Press.

[26]

McAllester, D. A., and R. Givan, R. 1992. Natural Language Syntax and First-Order Inference. Artificial Intelligence, 56(1): 1-20.

Digital Library

[27]

McCarthy, J. 1990. Formalizing Common Sense, ed. V. Lifschitz and J. McCarthy. Norwood, New Jersey: Ablex Publishers.

[28]

Mesiar, R., and H. Thiele. 2000. On T-Quantifiers and S-Quantifiers. In Discovering the World with Fuzzy Logic: Studies in Fuzziness and Soft Computing (57), ed. V. Novak and I. Perfilieva, 310-318. Heidelberg: Physica-Verlag.

[29]

Novak, V., and I. Perfilieva, eds. 2000. Discovering the World with Fuzzy Logic: Studies in Fuzziness and Soft Computing. Heidelberg: Physica-Verlag.

[30]

Novak, V. 1991. Fuzzy Logic, Fuzzy Sets, and Natural Languages. International Journal of General Systems, 20(1): 83-97.

[31]

Partee, B. 1976. Montague Grammar. New York: Academic Press.

[32]

Pedrycz, W., and F. Gomide. 1998. Introduction to Fuzzy Sets. Cambridge, Mass.: The MIT Press.

[33]

Peterson, P. 1979. On the Lqgic of Few, Many and Most. Journal of Formal Logic 20(1-2): 155-179.

[34]

Shafer, G. 1976. A Mathematical Theory of Evidence. Princeton, New Jersey: Princeton University Press.

[35]

Smith, B., and C. Welty. 2002. What Is Ontology? Ontology: Towards a New Synthesis. In Proceedings of the Second International Conference on Formal Ontology in Information Systems. New York: Association for Computing Machinery.

[36]

Smith, M. K., C. Welty, and D. McGuinness, eds. 2003. OWL Web Ontology Language Guide. W3C Working Draft 31. Cambridge, Mass.: World Wide Web Consortium (W3C).

[37]

Sowa, J. F. 1999. Ontological Categories. In Shapes of Forms: From Gestalt Psychology and Phenomenology to Ontology and Mathematics, ed. L. Albertazzi, 307-340. Dordrecht, The Netherlands: Kluwer Academic Publishers.

[38]

Sowa, J. F. 1991. Principles of Semantic Networks: Explorations in the Representation of Knowledge. San Francisco: Morgan Kaufmann Publishers.

[39]

Sowa, J. F. 1984. Conceptual Structures: Information Processing in Mind and Machine. Reading, Mass.: Addison-Wesley.

[40]

Sukkarieh, J. 2003. Mind Your Language! Controlled Language for Inference Purposes. Paper presented at the Joint Conference of the Eighth International Workshop of the European Association for Machine Translation and the Fourth Controlled Language Applications Workshop, Dublin, Ireland, 15-17 May.

[41]

Sun, R. 1994. Integrating Rules and Connectionism for Robust Commonsense Reasoning. New York: John Wiley.

[42]

Yager, R. R. 1991. Deductive Approximate Reasoning Systems. IEEE Transactions on Knowledge and Data Engineering 3(4): 399-414.

Digital Library

[43]

Zadeh, L. A. 2002. Toward a Perception-Based Theory of Probabilistic Reasoning with Imprecise Probabilities. Journal of Statistical Planning and Inference 105(1): 233-264.

[44]

Zadeh, L. A. 2001. A New Direction in AI--Toward a Computational Theory of Perceptions. AI Magazine 22(1): 73-84.

[45]

Zadeh, L. A. 2000. Toward a Logic of Perceptions Based on Fuzzy Logic. In Discovering the World with Fuzzy Logic: Studies in Fuzziness and Soft Computing (57), ed. V. Noyak and I. Perfilieva, 4-25. Heidelberg: Physica-Verlag.

[46]

Zadeh, L. A. 1999. From Computing with Numbers to Computing with Words--From Manipulation of Measurements to Manipulation of Perceptions. IEEE Transactions on Circuits and Systems 45(1): 105-119.

[47]

Zadeh, L. A. 1997. Toward a Theory of Fuzzy Information Granulation and Its Centrality in Human Reasoning and Fuzzy Logic. Fuzzy Sets and Systems 90(2): 111-127.

Digital Library

[48]

Zadeh, L. A. 1986. Outline of a Computational Approach to Meaning and Knowledge Representation Based on the Concept of a Generalized Assignment Statement. In Proceedings of the International Seminar on Artificial intelligence and Man-Machine Systems, ed. M. Thoma and A. Wyner, 198-211. Heidelberg: Springer-Verlag.

[49]

Zadeh, L. A. 1984. Syllogistic Reasoning in Fuzzy Logic and Its Application to Reasoning with Dispositions. In Proceedings International Symposium on Multiple-Valued Logic, 148-153. Los Alamitos, Calif.: IEEE Computer Society.

[50]

Zadeh, L. A. 1983. A Computational Approach to Fuzzy Quantifiers in Natural Languages, Computers and Mathematics 9: 149-184.

[51]

Zadeh, L. A. 1979. A Theory of Approximate Reasoning. In Machine Intelligence 9, ed. J. Hayes, D. Michie, and L. I. Mikulich, 149-194. New York: Halstead Press.

[52]

Zadeh, L. A. 1968. Probability Measures of Fuzzy Events. Journal of Mathematical Analysis and Applications 23: 421-427.

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Index Terms

Precisiated natural language (PNL)
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Probabilistic reasoning
      2. Vagueness and fuzzy logic
    2. Natural language processing
  2. Computer graphics
    1. Graphics systems and interfaces
      1. Perception
2. Theory of computation
  1. Logic

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Published In

cover image AI Magazine

AI Magazine Volume 25, Issue 3

Fall 2004

184 pages

ISSN:0738-4602

EISSN:2371-9621

Issue’s Table of Contents

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John Wiley & Sons, Inc.

United States

American Association for Artificial Intelligence

United States

Publication History

Published: 01 September 2004

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Cited By

Siddique BSufyan Beg M(2023)Reverse Dictionary Formation: State of the Art and Future DirectionsSN Computer Science10.1007/s42979-022-01495-14:2Online publication date: 17-Jan-2023
https://dl.acm.org/doi/10.1007/s42979-022-01495-1
Wang YPeng JPatel SGavrilova MFiorini RWidrow BHoward NKacprzyk JFrieder OSheu P(2018)Cognitive InformaticsInternational Journal of Cognitive Informatics and Natural Intelligence10.4018/IJCINI.201801010112:1(1-13)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.4018/IJCINI.2018010101
Novák V(2017)Fuzzy logic in natural language processing2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ-IEEE.2017.8015405(1-6)Online publication date: 9-Jul-2017
https://dl.acm.org/doi/10.1109/FUZZ-IEEE.2017.8015405
Yager RAlajlan N(2017)Approximate reasoning with generalized orthopair fuzzy setsInformation Fusion10.1016/j.inffus.2017.02.00538:C(65-73)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1016/j.inffus.2017.02.005
Budin GGavrilova MShell DWang YFiorini RWidrow BZadeh LHoward NWood SBhavsar VChan C(2016)Cognitive IntelligenceInternational Journal of Cognitive Informatics and Natural Intelligence10.4018/IJCINI.201610010110:4(1-20)Online publication date: 1-Oct-2016
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Wang Y(2016)On Cognitive Foundations and Mathematical Theories of Knowledge ScienceInternational Journal of Cognitive Informatics and Natural Intelligence10.4018/IJCINI.201604010110:2(1-25)Online publication date: 1-Apr-2016
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Štěpnička MBurda MŠtěpničková L(2016)Fuzzy rule base ensemble generated from data by linguistic associations miningFuzzy Sets and Systems10.1016/j.fss.2015.04.019285:C(140-161)Online publication date: 15-Feb-2016
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Wang Y(2015)Cognitive Learning Methodologies for Brain-Inspired Cognitive RoboticsInternational Journal of Cognitive Informatics and Natural Intelligence10.4018/IJCINI.20150401039:2(37-54)Online publication date: 1-Apr-2015
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Wang QKilgour DHipel K(2015)Facilitating risky project negotiationInformation Sciences: an International Journal10.1016/j.ins.2014.10.049295:C(544-557)Online publication date: 20-Feb-2015
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Novák V(2015)Evaluative linguistic expressions vs. fuzzy categoriesFuzzy Sets and Systems10.1016/j.fss.2015.08.022281:C(73-87)Online publication date: 15-Dec-2015
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